RankNexa
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Built around relevance. Designed for your product.

Make every
recommendation count.

Turn user signals into smarter discovery.
Recommendation and ranking infrastructure for experiences that feel relevant.

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Your catalog. Your context.A conversation, no commitment.
+RELEVANCE, MADE VISIBLEEXPLORE THE INTERACTIVE EXAMPLE
Interactive illustration
PERSONALIZATION EXPLORER

Same catalog. A different experience.

Session aware
Explore a session
SESSION CONTEXTBuilding a focused workspace
SignalsRankNexaRelevance
01Recent session intent
DEMO CATALOG ITEM A

Room to focus

02Related category
DEMO CATALOG ITEM B

A lighter workspace

03Discovery diversity
DEMO CATALOG ITEM C

Everyday carry

Recent actions change the context. Relevant workspace items move to the front.
Catalog → candidates → ranking → experienceSample items · illustrative logic · no live customer data

DESIGNED FOR TEAMS
where discovery matters.

Digital commerceMarketplacesContent & mediaSaaS & learning
The discovery gap

Great catalogs deserve
better than generic lists.

The right item is already in your catalog. Help the right person find it.

As catalogs grow, more choice can mean less clarity. RankNexa brings behavior, content, and context together so discovery can reflect what matters in the moment.

One relevance layer

Personal to your users. Practical for your team.

Recommendation intelligence designed around the way your product works.

Context changes.
Discovery should, too.

Combine recent actions, item attributes, and historical interests to make each discovery moment more relevant.

Explore personalization

Your rules.
In the ranking.

Balance relevance with eligibility, inventory, diversity, and merchandising priorities.

Better questions.
Measurable learning.

Compare ranking strategies with clear objectives, sensible baselines, and guardrail metrics.

How it works

From data to discovery.

One connected workflow. A clear role for every signal.

Explore the workflow
CatalogEventsContext
RankNexaCatalog · Events · Context
01 Relevant item02 Related discovery03 Something new

Conceptual workflow. Integration scope and capabilities are discussed for your use case.

Built for your discovery moment

Different products. The same need for relevance.

Commerce

A better match for every shopper.

Make product recommendations reflect browsing context, catalog attributes, and changing customer intent.

  • Homepage recommendations
  • Similar products
  • Cart discovery
Explore commerce
For the builders

Your product.
Our relevance layer.
A clearer connection.

Explore an API approach for catalogs, events, recommendations, and re-ranking. Keep the experience yours, with relevance built into the workflow.

  • Clear inputs and useful response shapes
  • Context and business rules in view
  • Evaluation as part of the integration
Explore the developer guide
POST /v1/recommendationsContent-Type: application/jsonAuthorization: Bearer <YOUR_API_KEY> {  "placement": "product_detail",  "user_id": "demo_user_42",  "session_id": "demo_session_12",  "context": {    "item_id": "sku_1048",    "locale": "en-IN"  },  "limit": 4}
Illustrative API example · schema subject to implementation

Relevance with responsibility.

Thoughtful use of signals. Understandable ranking objectives. Clear discussions about data before integration.

Our approach to trust
The relevance notebook

Good discovery starts with understanding.

All resources
Relevance fundamentals

Retrieval finds the options. Ranking finds the order.

Read the guideData & context

Useful discovery starts before a long user history.

Read the guideExperimentation

Define a useful recommendation before measuring it.

Read the guide
A little more clarity

Good questions. Clear answers.

Have something more specific in mind?

Let’s talk
01What is RankNexa?

RankNexa is focused on AI recommendation and personalization infrastructure. Its approach brings catalog data, behavioral signals, and contextual ranking together to help digital products deliver more useful discovery experiences.

02Who is RankNexa designed for?

Product, engineering, growth, and ML teams at commerce businesses, marketplaces, content platforms, and SaaS products. A useful starting point is a catalog, an interaction journey, and a clear relevance objective.

03What recommendation patterns can we explore?

Personalized suggestions, similar products, related content, contextual feeds, and re-ranking an existing set of search results. A discovery meeting helps determine which pattern fits your data and product.

04What if we have limited user history?

The architecture considers content attributes and session context alongside behavioral history. These approaches can help with new visitors and new items; their usefulness depends on your data and implementation.

05Can we define our own ranking rules?

The product direction includes eligibility filters, availability constraints, category balancing, diversity, and configurable ranking objectives. Discuss the specific controls your experience needs with our team.

06How should we approach customer data?

Start with the minimum signals needed for the use case. Data access, retention, processing requirements, and security responsibilities should be agreed before sharing production data. No customer records or credentials are needed to request a meeting.

Let’s find your starting point

Great catalogs.
Even better discovery.

Bring your product, your catalog, and your biggest relevance question. Let’s talk about what a better experience could look like.

Book a demo